#ifndef CAFFE_FCN_ACCURACY_LAYER_HPP_
#define CAFFE_FCN_ACCURACY_LAYER_HPP_

#include <vector>

#include "caffe/blob.hpp"
#include "caffe/layer.hpp"
#include "caffe/proto/caffe.pb.h"

#include "caffe/layers/loss_layer.hpp"

namespace caffe {

  /**
  * @brief Computes the classification accuracy for a one-of-many
  *        classification task.
  */
  template <typename Dtype>
  class FCNAccuracyLayer : public Layer<Dtype> {
  public:
    /**
    * @param param provides AccuracyParameter accuracy_param,
    *     with AccuracyLayer options:
    *   - top_k (\b optional, default 1).
    *     Sets the maximum rank @f$ k @f$ at which a prediction is considered
    *     correct.  For example, if @f$ k = 5 @f$, a prediction is counted
    *     correct if the correct label is among the top 5 predicted labels.
    */
    explicit FCNAccuracyLayer(const LayerParameter& param)
      : Layer<Dtype>(param) {}
    virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top);
    virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top);

    virtual inline const char* type() const { return "FCNAccuracy"; }
    virtual inline int ExactNumBottomBlobs() const { return -1; }
    virtual inline int MinBottomBlobs() const { return 2; }
    virtual inline int MaxBottomBlobs() const { return 3; }

    // If there are two top blobs, then the second blob will contain
    // accuracies per class.
    virtual inline int MinTopBlobs() const { return 1; }
    virtual inline int MaxTopBlos() const { return 2; }

  protected:
    /**
    * @param bottom input Blob vector (length 2)
    *   -# @f$ (N \times C \times H \times W) @f$
    *      the predictions @f$ x @f$, a Blob with values in
    *      @f$ [-\infty, +\infty] @f$ indicating the predicted score for each of
    *      the @f$ K = CHW @f$ classes. Each @f$ x_n @f$ is mapped to a predicted
    *      label @f$ \hat{l}_n @f$ given by its maximal index:
    *      @f$ \hat{l}_n = \arg\max\limits_k x_{nk} @f$
    *   -# @f$ (N \times 1 \times 1 \times 1) @f$
    *      the labels @f$ l @f$, an integer-valued Blob with values
    *      @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
    *      indicating the correct class label among the @f$ K @f$ classes
    * @param top output Blob vector (length 1)
    *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
    *      the computed accuracy: @f$
    *        \frac{1}{N} \sum\limits_{n=1}^N \delta\{ \hat{l}_n = l_n \}
    *      @f$, where @f$
    *      \delta\{\mathrm{condition}\} = \left\{
    *         \begin{array}{lr}
    *            1 & \mbox{if condition} \\
    *            0 & \mbox{otherwise}
    *         \end{array} \right.
    *      @f$
    */
    virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top);


    /// @brief Not implemented -- AccuracyLayer cannot be used as a loss.
    virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
      for (int i = 0; i < propagate_down.size(); ++i) {
        if (propagate_down[i]) { NOT_IMPLEMENTED; }
      }
    }

    int label_axis_, outer_num_, inner_num_;

    int top_k_;

    /// Keeps counts of the number of samples per class.
    Blob<Dtype> nums_buffer_;
  };

}  // namespace caffe

#endif  // CAFFE_FCN_ACCURACY_LAYER_HPP_
